Systems and methods for detecting the presence of a biological status using clustering

ABSTRACT

A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element (e.g., mecA), a plurality of second input values associated with a second genetic element (femA), and a plurality of third input values associated with a third genetic element (e.g., orfX) associated with a plurality of samples. Each sample includes a first input value in the plurality of first input values, a second input value in the plurality of second input values, and a third input value in the plurality of third input values. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples. The first and second boundary spaces differentiate a biological entity from other biological statuses. Other embodiments may also include the use of a genetic element such as SCCmec.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional of, and claims the benefit under35 USC 119(e) of U.S. Provisional Application No. 61/261,147, filed onNov. 13, 2009, which is herein incorporated by reference in its entiretyfor all purposes.

BACKGROUND

Methicillin resistant strains of Staphylococcus aureus (MRSA) areimplicated in infections with serious outcomes including nosocomialoutbreaks, and show resistance to a wide range of antibiotics, thuslimiting the treatment options. Healthcare associated MRSA is ofparticular clinical importance, because it is not only predictably crossresistant to all penicillins and cephalosporins, but is also typicallyresistant to multiple other commonly used antibiotics. Treatment of MRSAinfections generally requires more expensive and often more toxicantibiotics, which are normally used as the last line of defense.Therefore, rapid detection of MRSA is clinically crucial for bothtreatment and infection control measures.

Detection of MRSA is further complicated by the fact that MRSA can oftenco-colonize with multiple other related bacteria, includingmethicillin-sensitive Staphylococcus aureus (MSSA),methicillin-resistant coagulase-negative staphylococci (MR-CoNS) and/ormethicillin-sensitive coagulase-negative staphylococci (MS-CoNS).

Traditional methods for the detection of MRSA in clinical microbiologylaboratories involve culturing the bacteria from a sample as the firststep for the isolation and differentiation of MRSA from MSSA andMR-CoNS. This approach is time-consuming and requires a minimum of 20 to24 hours until a result is known.

A number of molecular based methods have been published for thedetection of methicillin resistant Staphylococcus aureus (MRSA) anddifferentiating it from methicillin sensitive Staphylococcus aureus(MSSA). One such method targets two separate regions of MRSA, the mecAgene of the Staphylococcus cassette chromosome (SCCmec, responsible formethicillin resistance) and spa gene of Staphylococcus aureus (U.S. Pat.No. 5,702,895, Sinsimer, et al., Journal of Clinical Microbiology,September 2005, 4585-4591). Unambiguous detection of MRSA using thisapproach is hampered by the co-existence of non-Staphylococcus aureusstrains such as methicillin resistant coagulase negative Staphylococci(MR-CoNS) which also harbors the mecA gene for methicillin resistance(Becker, et. al. Journal of Clinical Microbiology, January 2006, p229-231).

A more recent molecular approach utilizes primers and probes to SCCmecand the flanking region of the Staphylococcus aureus genome (U.S. Pat.No. 6,156,507, Hutletsky, et. al. Journal of Clinical Microbiology, May2004, p 1875-1884). SCCmec is a mobile genetic element that carries themecA gene and inserts at a specific site, attBscc, at the 3′-end of theorfX gene. The left extremity of SCCmec is contiguous with the non-orfXside of attBscc, while the right extremity of SCCmec is contiguous withthe orfX side of attBscc (Ito, et al., Antimicrob. Agent Chemother.2001, 45, p 1323-1336; Ito et al., Antimicrob. Agent Chemother. 2004,48, p 2637-2651, Noto, et al., J. Bacteriol. 2008, 190:1276-1283). Thisapproach infers the presence of the mecA gene from the detection of theSCCmec/orfX junction. This approach requires the use of multiple primersas there have been several different types of SCCmec described. Thisapproach is also subject to false positive results due to the presenceof SCCmec cassettes that do not contain the mecA gene (Farley, et. al.Journal of Clinical Microbiology, February 2008, p 743-746) and falsenegative results due to newly emerged SCCmec types not covered by theassay (Heusser, et al., Antimicrob. Agents Chemother. January 2007, p390-393).

Another approach utilizes one primer in a region of high homology acrossthe different SCCmec types and one primer in the flanking Staphylococcusaureus DNA (Cuny, et al. Clin. Microbiol Infect 2005; 11:834-837,European Patent 1529847 B1). This approach is also subject to falsepositives as the probability of also priming of MSSA is high withprimers encompassing this region.

Finally, a method has been described that positively selects forStaphylococcus aureus using specific antibodies and magnetic beads(Francois, et al. Journal of Clinical Microbiology, January 2003, p254-260; European Patent 1,370,694B1). This approach enriches forStaphylococcus aureus but requires the use of three primer/probe sets topositively identify MRSA and reduces the possibility of detecting CoNS.The method requires a centrifugation step and a separate lysis step torecover the nucleic acid.

The commercially available MRSA assays target the SCCmec right extremityjunction and orfX. Five different types and numerous subtypes of SCCmechave been identified and the potential of emergence of new SCCmecsubtypes is high. In addition, it is possible that MSSA derived fromMRSA might retain part of the SCCmec sequence without the mecA gene.Therefore, assays targeting the SCCmec right extreme junction with orfXare likely to give false positive results with MRSA-derived MSSA andfalse negative results with MRSA carrying newly emergent SCCmectypes/subtypes.

Thus, current methods for detection of MRSA are laborious and,time-consuming, and may not be particularly accurate. Accordingly, thereexists a need for a method and system that is fast, easy to use,reliable and capable of detecting and concurrently distinguishing abiological entity such as MRSA from other related bacteria, includingMSSA, and/or MR-CoNS, or other biological entities.

BRIEF SUMMARY

Embodiments of the present invention relate to systems and methods fordetermining if a biological status is, or is not, present in a sample.Some embodiments of the invention can be directed to a method includingdetecting methicillin resistant Staphylococcus aureus (MRSA) in a samplewhich may additionally contain methicillin sensitive Staphylococcusaureus (MSSA), methicillin resistant coagulase-negative staphylococci(MR-CoNS), and/or other strains of bacteria.

One embodiment of the invention is directed to a method (of creating ananalytical model) including entering into a digital computer, at least aplurality of first input values associated with a first genetic element(e.g., mecA), a plurality of second input values associated with asecond genetic element (e.g., femA), and a plurality of third inputvalues associated with a third genetic element (e.g., orfX) computerassociated with a plurality of samples. Each sample includes a firstinput value in the plurality of first input values, a second input valuein the plurality of second input values, and a third input value in theplurality of third input values. The method also includes determining athreshold value associated with the third genetic element, separatingthe samples using the threshold value into a first set of samples and asecond set of samples, clustering the first set of samples in a featurespace defined by the first genetic element and the second geneticelement, defining a first boundary space using the first set of samples,and defining a second boundary space using the second set of samples.The first and second boundary spaces differentiate a biological statusfrom other biological statuses.

Another embodiment of the invention is directed to a method of creatingan analytical model, which differentiates a biological status from otherbiological statuses. The method includes entering, into a digitalcomputer, at least a plurality of first input values associated with afirst genetic element (e.g., mecA), a plurality of second input valuesassociated with a second genetic element (e.g., femA), and a pluralityof third input values associated with a third genetic element (e.g.,SCCmec) into a digital computer. The method also comprises creating oneor more intermediate values using at least the plurality of first inputvalues and at least the second input values associated with a secondgenetic element using the digital computer, and creating a boundaryspace for the biological status using the one or more intermediatevalues and the plurality of third input values using the digitalcomputer. The boundary space differentiates the biological status fromother biological statuses.

Other embodiments of the invention are directed to methods for using theanalytical models as well as computer readable media (e.g.,non-transitory computer readable media such as memory chips and memorydisks) and systems that use the analytical models.

These embodiments, as well as other embodiments, will be described inmore detail later in this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart illustrating the steps taken according to oneembodiment for forming an analytical model.

FIG. 2 shows a diagram illustrating femA vs. mecA in two-dimensionalspace above and below a threshold value of orfX according to oneembodiment.

FIGS. 3A and 3B show diagrams illustrating femA vs. mecA intwo-dimensional space according to one embodiment.

FIG. 4 shows a flowchart illustrating the steps taken according to oneembodiment of using an analytical model.

FIG. 5 shows a flowchart illustrating the steps taken according to oneembodiment for forming an analytical model using an intermediate value.

FIGS. 6A and 6B shows show diagrams illustrating mecA vs. SCCmec andfemA vs. SCCmec in two-dimensional space according to one embodiment.

FIG. 7 shows a diagram illustrating a method for creating anintermediate value and using the intermediate value with SCCmec tocreate a boundary space according to one embodiment.

FIG. 8 shows a diagram illustrating an intermediate value vs. SCCmec intwo-dimensional space according to one embodiment.

FIG. 9 illustrates a boundary space as a function of an intermediatevalue vs. SCCmec in two-dimensional space according to one embodiment.

FIG. 10 shows a flowchart illustrating the steps taken according oneembodiment for using an analytical model.

FIG. 11 is a block diagram of a system that can be used to executevarious embodiments.

DETAILED DESCRIPTION

Various embodiments disclose systems and methods for identifyingMethicillin resistant strains of Staphylococcus aureus (MRSA) in asample from measured amounts of genetic elements such as mecA, SCCmec,orfX, and/or a Staphylococcus aureus-specific target gene sequence suchas femA in the sample. Some embodiments of the invention determine thepresence of MRSA in the sample by using a first boundary space todetermine the presence of MRSA when the detected amount of orfX is belowa threshold value and a second boundary space to determine the presenceof MRSA when the detected amount of orfX is above the threshold value.Other embodiments create an intermediate value from at least mecA andthe Staphylococcus aureus-specific target gene sequence and use aboundary space to determine the presence of MRSA. The boundary space canbe defined using the intermediate value and SCCmec.

All scientific and technical terms used in this disclosure have meaningscommonly used in the art unless otherwise specified. As used in thisdisclosure, the following words or phrases have the meanings specified.

As used herein, the term “sample” is used in its broadest sense, andrefers to any type of material of biological origin, which can be, forexample, any fluid, tissue, or cell. For example, a sample can be abiological fluid, e.g., urine, blood, serum, plasma, nasal secretion,cerebrospinal fluid, etc. Alternatively, a sample can be cultured cellsor tissues, cultures of microorganisms, or any fraction or productsproduced from or derived from biological materials. Optionally, a samplecan be purified, partially purified, unpurified, enriched or amplified.

The term “genetic element” as used herein can refer to a subsequence ina genome of interest that is useful as a target in the methods of theinvention. In some embodiments, the genetic element is an open readingframe or gene, such as, for example, orfX, femA or mecA inStaphylococcus. A genetic element may also be a mobile genetic element,such as the Staphylococcus cassette chromosome, SCCmec, which may or maynot comprise the mecA gene.

As used herein, “input values” may be any suitable values that can beassociated with, for example, genetic elements. For example, inputvalues can be Ct values associated that various target genes.

As used herein, a “boundary space” can be a space defined by a “boundaryfunction.” A “boundary function” can be a mathematical function that isused to determine whether data is associated with a biological status oris not associated with a biological status. Boundary functions may becreated in any suitable manner including manually, by the use of neuralnetworks, cost functions, etc. Boundary functions may also representedby any suitable shape or line, including an ellipse, rectangle, circle,or the like. Boundary functions may also be regular or irregular inshape.

As used herein, the terms “SCCmec” “SCCmec sequence” and “SCCmeccassette” are used interchangeably to refer to the genetic element knownas the Staphylococcus cassette chromosome, which carries the mecA geneand is inserted into Staphylococcus sp. genome as described in Ito etal. (2001, Antimicrob. Agents Chemother. 45:1323-1336). In oneembodiment, SCCmec is at an orfX junction.

The SCCmec insertion site is referred to as “orfX-ISS/attBscc” in thisapplication. The insertion site is at the 3′ end of a Staphylococcusaureus gene referred to herein, as “orfX”. The chromosomal locus whereSCCmec insertion takes place is referred to as “attBscc.”. The specificsequence at the insertion site is referred to here as the“orfX-Insertion Site Sequence (orfX-ISS)” or “attBscc core region.” Thissequence is known to be a highly conserved sequence in Staphylococcusaureus (Ito, et al., Antomicrob. Agent Chemother. 2001, 45, p 323-1336,Noto, et al., J. Bacteriol. 2008, 190:1276-1283).

After insertion into the orfX-ISS/attBscc region of Staphylococcusaureus, the SCCmec left extremity junction region is referred to asMRSA-LE and the right extremity junction region is referred to asMRSA-RE. In the left extremity junction, the SCCmec sequence iscontiguous with the non-orfX side of attBscc. In the right extremejunction, the SCCmec sequence is contiguous with the orfX-side ofattBscc. The orfX-ISS/attBscc region is described in detail in Ito etal. (2001, Antimicrob. Agents Chemother. 45:1323-1336; Ito et al.,Antimicrob. Agent Chemother. 2004, 48, p 2637-2651, Noto, et al., J.Bacteriol. 2008, 190:1276-1283) and in U.S. Pat. No. 6,156,507, all ofwhich are incorporated by reference herein. If the SCCmec insertion isnot present, the orfX-ISS/attBscc region is uninterrupted. If theorfX-ISS/attBscc region is identified as intact through an amplificationmethodology this indicates that the SCCmec cassette has not beeninserted. Lack of amplification of the orfX-ISS/attBscc region, however,does not indicate the mecA gene is present. It is known the mecA genecan be lost after the SCCmec cassette becomes inserted. Thus the SCCmeccassette can still prevent amplification of the orfX-ISS/attBscs regioneven in the absence of mecA.

An “oligonucleotide” is a nucleotide polymer having two or morenucleotide subunits covalently joined together. Oligonucleotides aregenerally about 10 to about 100 nucleotides. The sugar groups of thenucleotide subunits may be ribose, deoxyribose, or modified derivativesthereof such as OMe. The nucleotide subunits may be joined by linkagessuch as phosphodiester linkages, modified linkages or by non-nucleotidemoieties that do not prevent hybridization of the oligonucleotide to itscomplementary target nucleotide sequence. Modified linkages includethose in which a standard phosphodiester linkage is replaced with adifferent linkage, such as a phosphorothioate linkage, amethylphosphonate linkage, or a neutral peptide linkage. Nitrogenousbase analogs also may be components of oligonucleotides in accordancewith the invention. A “target nucleic acid” is a nucleic acid comprisinga target nucleic acid sequence. A “target nucleic acid sequence,”“target nucleotide sequence” or “target sequence” is a specificdeoxyribonucleotide or ribonucleotide sequence that can be hybridized toa complementary oligonucleotide.

As used herein, the term “probe” refers to an oligonucleotide which iscapable of hybridizing to a target nucleic acid of interest. Thehybridization occurs as a result of the probe binding throughcomplementary base pairing to a target nucleic acid of interest. It willbe understood by one skilled in the art that probes will typicallysubstantially bind target sequences lacking complete complementaritywith the probe sequence depending upon the stringency of thehybridization conditions. The probe may be associated with a suitablelabel or reporter moiety so that the probe (and therefore its target)can be detected, visualized, measured and/or quantitated.

As used herein, the term “primer” refers to an oligonucleotide used toprime nucleic acid synthesis. A primer hyrbridizes to the templatethrough complementary base pairing and is therefore used to initiate thereplication. Hybridization occurs in the same manner as that describedfor probes, above. In PCR, two primers are used: a “forward primer” thattypically hybridizes to the sense strand and a “reverse primer” thattypically hybridizes to the antisense strand.

As used herein, the term “PCR” refers to a technique for exponentialamplification of short DNA sequences (usually 50 to 600 bases) within alonger double stranded DNA molecule by enzymatic replication of DNAwithout using a living organism (Mullis et al. Methods Enzymol. 1987;155:335-50). Other in vitro amplification technologies can be used inthe present invention and are well known to those of skill. Thesemethods include, for example, Ligase Chain Reaction (LCR), Nucleic AcidsSequence Based Amplification (NASBA), Strand Displacement Amplification(SDA), Transcription Mediated Amplification (TMA), Branched DNAtechnology (bDNA) and Rolling Circle Amplification Technology (RCAT).

As used herein the term “Real-Time PCR” refers to a type of PCR wherethe amplified DNA is quantified as it accumulates in the reaction inreal time after each amplification cycle (Heid et al, Genome Research,1996 6(10):986-994). A number of probe chemistries for carrying outReal-Time PCR are well known to those of skill. One commonly used methodis the TaqMan® assay (see, e.g., U.S. Pat. Nos. 5,210,015; 5,487,972;and 5,804,375). Other Real-Time PCR probe chemistries that can be usedand can be purchased commercially include FRET primers, MolecularBeacons, Scorpion Primers®, Amplifluor Primers®, LUX Primers®, Eclipse®,and Ultimate Probe®. For a review of Real-Time PCR techniques see Bustinet al., J. Mol. Endocrin. 34:597-601 (2005).

As used herein, the term “multiplex PCR” refers to a type of PCR wheremore than one set of primers is included in a reaction allowing two ormore different targets to be amplified in a single reaction tube. Theterm “multiplex PCR” also refers to a PCR where multiple primers andprobes are used but only one target is amplified. In one embodiment, themultiplex PCR of the present invention is a real-time PCR.

As used herein, a “biological status” may relate to a particularbiological state of a sample derived from any source, for example, apatient. In most cases, the biological status relates to whether or notthe sample comprises a particular biological entity, for example, atarget disease organism or patient cell associated with disease. Forexample, one biological status may be that a sample is comprises MRSAbacteria, while another biological status may be that the sample doesnot comprise MRSA bacteria. In other examples, the biological status mayrelate to whether or not the sample comprises cancer cells.

One embodiment of the invention relates to an assay for detection ofMRSA in a sample that may contain MRSA, MSSA, MR-CoNS, or otherbacteria. Embodiments of the invention can utilize a multiplex PCRprocess for simultaneously amplifying and detecting a combination ofmultiple targets.

According to one embodiment, the initial amount of target DNA ismeasured by the PCR threshold cycle (Ct). For example, a defined signalthreshold is determined for a reaction to be analyzed. The number ofcycles (Ct) required to reach this signal threshold is determined forthe target nucleic acid as well as for a reference or standard nucleicacid. The absolute or relative copy numbers of the target molecule canbe determined on the basis of the Ct values obtained for the targetnucleic acid as compared to the reference nucleic acid The Ct value isthus inversely proportional to the amount of initial target DNA, seeHeid et al, 1996, Genome Research 6(10):986 for a full discussion of theCt value which is incorporated herein by reference. Other mathematicalapproaches can be employed which allow for the extrapolation of theinitial amount of a particular target gene based upon the indication ofa predetermined set amount or number of genes amplified during one ofthe identified methods.

In one embodiment, the present invention is directed to a method ofdetermining the presence of MRSA in a sample, said method comprisingsubjecting the sample to real-time PCR for a time and under conditionsso as to generate a level of amplification product which is sufficientto be detected by fluorescence and is indicative of initial level of oneor more MRSA-specific target sequences in the sample.

In another embodiment, the amplification is conducted with a set ofprimers (forward and reverse) and a probe. The probe may be labeled witha fluorogenic reporter molecule at its 5′ end and a quenching moleculeat its 3′ end. The quenching molecule prevents emission of signal fromthe fluorogenic reporter molecule. The probe hybridizes to a region ofthe target sequence between the regions to which the forward and reverseprimers hybridize. As the polymerase moves along the strand to which theprobe has hybridized, the 5′ end of the probe is cleaved off by theexonuclease activity of the polymerase thus permitting emission of thefluorogenic signal due to separation of the quenching moiety.

In specific embodiments, the probes of the invention may comprisedual-labeled fluorogenic probes comprising a fluorescent reporter(fluorophore) and a fluorescent or non-fluorescent quencher molecule.The fluorophores of the invention may be attached to the probe at anylocation, including the 5′ terminus, the 3′ terminus or internal toeither termini. In an embodiment of the invention, the fluorophore andquencher are attached to the 5′ and 3′ termini of the proberespectively. The examples of fluorophores include, but are not limitedto, FAM, ROX, HEX, NED, Cy5, Texas Red, Calfluor Red, CalFluor Orange,Quasar 670, Quasar 705. The examples of quenchers include, but are notlimited to, TAMARA, Blackhole quenchers BHQ-1, BHQ-2.

In another embodiment, the invention provides a method for detecting anddistinguishing MRSA from MSSA, MR-CoNS, or other bacteria utilizing athree target assay, wherein the targets (which may be examples ofgenetic elements) used in the assay include the mecA gene sequence, aStaphylococcus aureus-specific target gene sequence, an SCCmec genesequence, and/or orfX. In a specific embodiment, the Staphylococcusaureus-specific target gene is femA. In the descriptions below, femA isoften explicitly mentioned as the Staphylococcus aureus-specific targetgene sequence; however, other Staphylococcus aureus-specific target genesequences may also be used according to various embodiments.

Some embodiments of the invention are directed to the formation ofanalytical models using at least first and second boundary spaces, aswell as the use of such analytical models. Other embodiments of theinvention are directed to the creation and use of analytical models thatuse at least one intermediate value to form a boundary space. Yet otherembodiments relate to methods for using such analytical models as wellas systems using such analytical models. These approaches are describedin further detail below.

Embodiments Using at Least Two Boundary Spaces

FIG. 1 shows a flowchart, which illustrates steps that can be used tobuild an analytical model according to an embodiment of the invention.In some cases, the analytical model can be used to determine whetherMRSA is present in a sample.

At step 1000, a selected number of known samples are subjected toconditions that expose the nucleic acids of bacteria in the samples. Insome embodiments, a known sample is one in which it is known whether ornot it is associated with a particular biological status. For example, aknown sample may be one where it is known whether or not sample isassociated with MRSA. The known samples can thus be used to build amodel that can determine whether a later unknown sample also containsMRSA.

There are many different ways for subjecting a sample to conditions thatexpose the nucleic acids in the sample. For example, cells in the samplemay be lysed according to well known techniques. The nucleic acids maythen be denatured by, for example, raising the temperature of the sampleto separate strands of nucleic acids.

At step 1010, the characteristics (e.g., relative or absolute amount oramount of expression) of at least three targets, such as mecA, orfX, anda Staphylococcus aureus-specific target gene sequence, are measured in asample. According to one embodiment, the Staphylococcus aureus-specifictarget gene sequence is femA. There are many different ways to measurethe amounts of such genetic elements in a sample. For example, amultiplex PCR process can be used to measure the PCR threshold cycle(Ct) value for each target of the measurement.

After the characteristics of the at least three targets are measured,values associated with those characteristics may be entered into adigital computer. Details of an exemplary digital computer are providedbelow. The various input values may be entered into the digital computerin any suitable manner. In some embodiments, the values may be enteredinto the digital computer automatically (e.g., through a data connectionto a measurement module that creates the input values or the data usedto create the input values) or manually by a user.

In some embodiments, at least a plurality of first input valuesassociated with a first genetic element (e.g., a first target such asmecA), a plurality of second input values associated with a secondgenetic element (e.g., a second target such as femA), and a plurality ofthird input values associated with a third genetic element (e.g., athird target such as orfX) are entered into the digital computer. Thefirst, second, and third input values are associated with the pluralityof known samples. Each known sample includes a first input value in theplurality of first input values, a second input value in the pluralityof second input values, and a third input value in the plurality ofthird input values. The first, second and third values may be Ct valuesassociated with the first, second, and third genetic elements.

At step 1020, a call algorithm can be applied to each of the measuredtargets from each of the known samples. The call algorithm can have anysuitable combination of instructions. In some embodiments, the callalgorithm may include one or more of the steps 1030, 1040, 1050, and1060 in FIG. 1 in any suitable combination.

At step 1030, a threshold value associated with the third geneticelement (e.g., orfX) is determined. The threshold value may bedetermined in any suitable manner. For example, in some embodiments, thethreshold value may simply be a discrete value such as “45.” Thethreshold value may have been previously entered into the digitalcomputer and stored in a memory in the digital computer, or may beentered by a user at about the same time that the first input values,the second input values and the third input values are entered into thedigital computer.

At step 1040, the call algorithm separates the known samples using thethreshold value into a first set of samples and a second set of knownsamples. For example, the call algorithm may separate the first set ofknown samples from the second set of known samples by determining whichsamples fall above the threshold value and which samples fall below thethreshold value. Illustratively, if the third genetic element is orfXand the threshold is a Ct value of “45,” then the first set of knownsamples may have Ct values for orfX of less than 45 and the second setof known samples may have Ct values for orfX greater than or equal to45.

At step 1050, after the samples are separated, the samples in the firstset of samples are clustered in a feature space defined by the firstgenetic element and the second genetic element. The first and second setof samples may be clustered in any suitable manner. For example, thefirst set of samples may be plotted in two-dimensional space defined bythe first genetic element and the second genetic element. The second setof samples may be plotted in two-dimensional space defined by the firstgenetic element and the second genetic element. This is illustrated in,for example, FIG. 2, which shows two plots of femA vs. mecA for orfX <45and orfX >=45.

At step 1060, a first boundary space and a second boundary space aredefined using the first and second sets of samples, respectively. Thefirst and second boundary spaces differentiate a biological status fromother biological statuses. In FIG. 2, the first and second boundaryspaces are shown as ellipses.

The first and second boundary spaces can be determined in any suitablemanner and can have any suitable shape. For example, in someembodiments, the first and second boundary spaces can be defined byellipses. In other embodiments, the boundary spaces can be defined byrectangles, polygons, parallelepipeds, or other shapes. Such boundaryspaces can be determined and optimized by neural networks and otheroptimization algorithms. The first and second boundary spaces can format least part of an analytical model, which can be used to differentiatea biological status from other biological statuses.

In some cases, to assist a user, it may be desirable to graphicallydisplay the first and second boundary spaces over two dimensional plotsincluding the first and second sample sets on a display such as an LCDscreen.

Specific embodiments illustrating the method shown in FIG. 1 can now bedescribed. For a 3 target “Strategy A” algorithm, 296 specimen runs werecollected and provided for analytical model development. In the StrategyA implementation, the three targets corresponding to first, second, andthird genetic elements included mecA, femA, and orfX.

In any classification problem, when the appropriate characteristics aremeasured to distinguish one class of events from another, a uniquefeature space will exist that permits the data to be categorized. Thisis true for MRSA classification. To create the analysis routine for thisapplication, the feature space was observed as shown in FIG. 3A. In FIG.3A, both the positive and negative data are plotted in the femA-vs-mecAtwo-dimensional feature space. The positive data points cluster alongthe diagonal in the lower-left corner and the negative data pointsspread out more randomly, especially in the upper half space andlower-right corner.

Based on this and additional confirmatory data, a mathematical model wasselected to encapsulate the positive feature space. The data for thepositive events resembles an ellipse which was chosen to formulate theboundary between the positive and negative clusters. An ellipticboundary can be considered an ideal selection in that positive datausually forms a Gaussian distribution in a preferred feature space (notethat the cross-section of a Gaussian distribution representing theboundary of a feature space is an ellipse). This justifies theelliptical model in FIG. 3B. Mathematically, the equation for theelliptical model is given as:

${{\frac{x^{2}}{a^{2}} + \frac{y^{2}}{b^{2}}} = 1},$

where a is the semi-major axis and b is the semi-minor axis. In thisinstance, the ellipse is assumed to be centered about the origin. Tosupport the MRSA detection, the elliptical model needs to supporttranslations and angular displacement around an origin defined by(x₀,y₀) and having an angle of θ.

Thus, in its most general form, an ellipse can be completelycharacterized in a two dimensional feature space as:

${\frac{x^{\prime 2}}{a^{2}} + \frac{y^{\prime 2}}{b^{2}}} = 1$${where}\mspace{14mu} \{ \begin{matrix}{x^{\prime} = {{{- {\cos (\theta)}}x} + {{\sin (\theta)}y} - x_{0}}} \\{y^{\prime} = {{{\sin (\theta)}x} + {{\cos (\theta)}y} - y_{0}}}\end{matrix} $

The generalized ellipse can be governed by a set of five parameters, a,b, x₀, y₀, and θ. Once the set of parameters is determined a uniqueellipse (or feature space) is defined.

In order to obtain an optimal set of elliptical parameters, a costfunction can be defined based on the classification results. For thistype of application, a commonly used cost function is the area undercurve of the receiver operating characteristic or ROC curve (refer tohttp://en.wikipedia.org/wiki/Receiver_operating_characteristic for moredetails about ROC curves). The ROC curve provides a graphical plot ofthe sensitivity versus the 1-Specificity for the application. For anMRSA application, a combination of the number of false positives andnumber of false negatives is applied as the cost function:

cost=c ₁ * FN#+c ₂ *FP#.

The weighting factors c₁ and c₂ are chosen to represent the preferencein the particular problem.

Given the mathematical model for the feature space and the designed costfunction definition, the model for the feature space can be optimized tominimize the cost function. The realization of the model with theminimum cost is an optimal solution for the classification problem.There are several optimization procedures that can be utilized such asHill Climbing, Simulated Annealing, Genetic Algorithms, and so forth.For this application, a Genetic Algorithm is utilized. More detailedinformation about Genetic Algorithms can be found inhttp://en.wikipedia.org/wiki/Genetic_algorithms.

Referring to FIG. 2, it was observed that the features measured by thefemA-vs-mecA in two-dimensional feature space were not alwayshomogeneous or ideal for this model. For example, as shown in FIG. 2,when orfX is equal to or greater than 45, the positive data points formsa “fat” cluster (as outlined) but when orfX is less than 45, thepositive data points forms a cluster that is more inline with anellipse. As a result, two elliptical models are established for orfX <45and orfX >=45, respectively. With this orfX-dependent elliptical model,the data for MRSA positive samples is essentially classified in athree-dimensional space.

The graphical depiction of the analytical model illustrated in FIG. 2can then be used to classify unknown samples as being associated with aparticular biological status such as MRSA.

Generally, the use of the formed analytical model can include entering afirst input value, second input value, and third input value associatedwith an unknown sample into a digital computer. The digital computer maybe the same digital computer that is used to form the analytical model,or may be a different digital computer (e.g., when the analytical modelis formed on one digital computer, but is stored in another digitalcomputer where it is used). The first input value, the second inputvalue and the third input value are associated with the first, second,and third genetic elements in the unknown sample. After the input valuesare entered, the digital computer classifies the unknown sample as beingassociated with the biological status using the first boundary space orthe second boundary space using the analytical model.

FIG. 4 illustrates steps that can be used to determine whether MRSA ispresent in a sample according to one embodiment. As used herein, anunknown sample refers to a sample in which it is not known whether MRSAis present in the sample. The steps in FIG. 4 can use a model, such as amodel created using the steps from FIG. 1, to determine whether anunknown sample contains MRSA. The unknown sample can have varioustargets measured, and these measurements can be used to detect thepresence of MRSA by analyzing where an intermediate vector created fromthe measured targets of the unknown sample resides relative to aboundary function of the model.

At step 1100, the unknown sample is subjected to conditions that exposethe nucleic acids of bacteria in the sample. The same techniquesdescribed above with respect to step 1000 in FIG. 1 can also be used atstep 1100.

At step 1110, characteristics associated with at least three targets,mecA, fem A, and orfX, (or other genetic elements) can be determinedfrom the unknown sample. The same techniques used during step 1010 inFIG. 1 above can be used to accomplish step 1110. The characteristicscan be the same as the characteristics used to form the analyticalmodel. For example, if Ct values are used to form the analytical model,the Ct values can be determined for the three targets in step 1110.

At step 1120, the analytical model can be applied to the input valuesassociated with the three targets. The process associated with theanalytical model can include at least steps 1130 and 1140 in FIG. 4.

At step 1130, for example, the method determines whether a third inputvalue associated with orfX is above or below the threshold value (e.g.,“45”) in the analytical model.

At step 1140, if the third input value associated with the unknownsample is below a threshold value (e.g., 45), then the previously formedfirst boundary space is used to determine whether or not the sample isassociated with the biological status of interest (e.g., MRSA).Alternatively, if the third input value associated with the unknownsample is above a threshold value (e.g., 45), then the previously formedsecond boundary space is used to determine whether or not the sample isassociated with the biological status of interest (e.g., MRSA).

If desired, additional rules can be used to further classify the unknownsample. For example, one, two, or three or more additional targets(e.g., a target associated with MR-CoNs) may be used as additional data,which may be additionally used to classify the unknown sample.

Embodiments Using Intermediate Values

Another embodiment of the invention can be directed to a method forcreating an analytical model, which differentiates a biological statusfrom other biological statuses. The method uses at least oneintermediate value. Such embodiments can be described with reference toFIG. 5.

At step 5000, a selected number of known samples are subjected toconditions that expose the nucleic acids of bacteria in the samples. Thedetails of step 5000 can be the same or different than those describedwith respect to step 1000 in FIG. 1, and the descriptions above need notbe repeated.

At step 5010, characteristics of at least three targets, such as mecA,femA, and SCCmec, are measured for each of the known samples. Thedetails of step 5010 can be the similar to or different than thosedescribed above with respect to step 1010 in FIG. 1, and thedescriptions above need not be repeated. Note, however, that in thisexample, SCCmec is identified as a target instead of orfX.

After the input values are determined, they are entered into a digitalcomputer. In some embodiments, the input values include at least aplurality of first input values associated with a first genetic element(e.g., a first target such as mecA), a plurality of second input valuesassociated with a second genetic element (e.g., a second target such asfemA), and a plurality of third input values associated with a thirdgenetic element (e.g., a third target such as SCCmec) associated withknown samples into a digital computer. The first, second, and thirdinput values may be entered into the digital computer at any suitabletime and in any suitable order.

At step 5020, a call algorithm can be applied to each of the measuredtargets from each of the known samples. The call algorithm in thisexample, is different than the call algorithm described above withrespect to FIG. 1.

At step 5030, one or more intermediate values may be determined using atleast the plurality of first input values and at least the second inputvalues associated with a second genetic element using the digitalcomputer. For example, in some embodiments, first and second inputvalues (e.g., first and second Ct values) associated with a firstgenetic element (mecA) and a second genetic element (femA) may be usedto create one or more intermediate values, which may be combined withthe third input values associated with the third genetic element (e.g.,SCCmec).

As shown in step 5040, after the one or more intermediate values arecreated, a boundary space for the biological status is created using theone or more intermediate values and the plurality of third input valuesusing the digital computer. The boundary space differentiates thebiological status from other biological statuses.

Illustratively, for a 3 target “Strategy C” algorithm, 199 specimen runswere collected and provided for algorithm development. In the Strategy“C” implementation, the three targets were mecA, femA, and SCCmec.

The mecA-vs-SCCmec and femA-vs-SCCmec two-dimensional feature spaces areshown in FIGS. 6A and 6B. It is observed that there is a grey area inwhich the positives and negatives are mixed and cannot be distinguished(outlined by the black circles) in both of the two-dimensional featurespaces.

In order to overcome the grey area, a new parameter can be used todistinguish events as positive or negative. This new parameter, wasestablished as:

newParameter=mecA*sin(−0.3)+femA*cos(−0.3)

The “new parameter” is an example of an intermediate value, since it isderived from the first input values associated with a first geneticelement (e.g., mecA) and second input values associated with a secondgenetic element (e.g., femA).

FIG. 7 shows an illustration of how the new parameter can be created andused. FIG. 7 shows that values associated with mecA 810 and femA 820 canbe combined to form an intermediate value Y 840. This intermediate valueY 840 and SCCmec 830 can form a two dimensional feature space, which canbe used to define a boundary space for the classification of unknownsamples as being associated with MRSA or not MRSA.

A plot of the newParameter-vs-SCCmec feature space is provided in FIG.8. In this illustration, the grey area disappears and the new parameter,together with SCCmec, constitutes a better feature space forclassification.

Given the “newParameter” and SCCmec, an elliptical model (as shown inFIG. 9) was established to define the boundary between the positive andnegative data points. The elliptical model can be optimized with aGenetic-Algorithm in the same way as previously described. (E.g., any ofthe previously described boundary forming techniques, such as those instep 1060 above, can be used in embodiments of the invention.) All thedata points that are located inside the ellipse are deemed as MRSApositive.

FIG. 10 illustrates steps that can be used to determine whether MRSA ispresent in a sample according to one embodiment. As used herein, anunknown sample refers to a sample in which it is not known whether MRSAis present in the sample. The method can use an analytical model, suchas the model created using the steps from FIG. 5, to determine whetheran unknown sample contains MRSA (or is associated with anotherbiological status). The characteristics of various targets in theunknown sample can be measured to form first, second, and third inputvalues. Using the input values and the analytical model, it is possibleto detect the presence of MRSA in the unknown sample.

Referring to FIG. 10, at step 6000, the unknown sample is subjected toconditions that expose the nucleic acids of bacteria in the sample. Thesame techniques used during step 1000 in FIG. 1 can be used in step6000.

At step 6010, characteristics of at least three targets, mecA, andSCCmec are measured from the unknown sample. First, second, and thirdinput values can be determined for the unknown sample. The sametechniques described above with respect to step 1010 can be used toaccomplish step 6010.

At step 6020, analytical model and the first, second, and third inputvalues can be used to determine whether or not the particular biologicalstatus (e.g., MRSA or not MRSA) is present in the unknown sample.

At step 6030, when applying the analytical model to the first, second,and third input values, an intermediate value can be determined usingthe first, and second input values. For example, the following equationcould be used to determine the intermediate value:

Y=newParameter=mecA*sin(−0.3)+femA*cos(−0.3)

As an illustration, given a sample with mecA=28.49, femA=27.90, andSCCmec=27.26, Y=18.2345.

At step 6040, in the method, it is determined whether the sample ispositive for MRSA by using the boundary function that is in theanalytical model. For example, in the above example, the valuesY=18.2345 and SCCmec=27.26 can be compared against the ellipticalboundary function in FIG. 9. Since this example would fall inside of theboundary function, the unknown sample would be classified as MRSApositive.

Systems

FIG. 11 shows a system including a digital computer 300, and a measuringmodule 301 operatively coupled (which may include electronic coupling)to the digital computer 300.

In this example, the digital computer 300 may include a variety oftypical computer components including a system bus 304, one or more diskdrives 305, RAM 306, and a processor 307, operatively coupled together.Other components can also be present depending on the exact nature ofthe embodiment. FIG. 11 also shows a display 308, a keyboard 302, and amouse 303. These components and other components may also be part of thedigital computer in some embodiments.

The system can also have a measuring module 301 that is used to measurecharacteristics of selected targets in a sample (e.g., known orunknown). This measuring module may vary between different embodimentsof the invention depending on the measurement method selected to measurethe target responses. For example, according to one embodiment, themeasurement module may conduct a PCR analysis on a sample and maytherefore be a real-time PCR apparatus. Real-time PCR apparatuses arecommercially available.

In one embodiment of the invention, a sample is placed in themeasurement module 301 where the sample is processed and characteristicsof the selected targets (e.g., the quantities) from the sample aremeasured. This data (e.g., the previously described input values) isthen transferred into the digital computer 300 along a system bus 304,and an appropriate call algorithm or analytical model can be applied tothe response data using the processor 307. The instructions cause theprocessor 307 to execute the call algorithm or analytical model (asdescribed above), which may be stored on a computer readable medium suchas the RAM 306 or disk drive 305. The data representing the callalgorithm and/or the analytical model can also be stored on this samemedia. The output from the application of the call algorithm or theanalytical model can then be displayed on the display 308 or otheroutput device (e.g., a printer). For example, the previously describedboundary functions and their associated graphs may be displayed on thedisplay 308 or output in some other manner. Thus, the information fromthe measured sample can then be used to either help build a model ordetermine whether the sample contains MRSA.

As noted above, in some embodiments, the computer readable media maystore or include code which can be executed by the processor toimplement a method for forming an analytical model. In one embodiment,the method may include: entering into a digital computer, at least aplurality of first input values associated with a first genetic element,a plurality of second input values associated with a second geneticelement, and a plurality of third input values associated with a thirdgenetic element into a digital computer associated with a plurality ofknown samples, wherein each known sample includes a first input value inthe plurality of first input values, a second input value in theplurality of second input values, and a third input value in theplurality of third input values; determining a threshold valueassociated with the third genetic element; separating the known samplesusing the threshold value into a first set of known samples and a secondset of known samples; clustering the first set of known samples in afeature space defined by the first genetic element and the secondgenetic element; defining a first boundary space using the first set ofknown samples, wherein the first boundary space differentiates abiological status from other biological statuses; and defining a secondboundary space using the second set of known samples, wherein the secondboundary space differentiates a biological status from other biologicalstatuses. In another example, the method may include entering, into adigital computer, at least a plurality of first input values associatedwith a first genetic element, a plurality of second input valuesassociated with a second genetic element, and a plurality of third inputvalues associated with a third genetic element into a digital computer;creating one or more intermediate values using at least the plurality offirst input values and at least the second input values associated witha second genetic element using the digital computer; and creating aboundary space for the biological status using the one or moreintermediate values and the plurality of third input values using thedigital computer, wherein the boundary space differentiates thebiological status from other biological statuses.

As noted above, in some embodiments, the computer readable media maystore or include code which can be executed by the processor toimplement a method for using an analytical model. The method may includeentering a first input value, second input value, and third input valueassociated with an unknown sample into the digital computer or otherdigital computer, wherein the first input value, the second input valueand the third input value is associated with the first, second, andthird genetic elements in the unknown sample; and classifying theunknown sample as being associated with the biological status using thefirst boundary space or the second boundary space using the digitalcomputer or other digital computer. In another embodiment, the methodmay include: entering a first input value, second input value, and thirdinput value associated with an unknown sample into the digital computeror other digital computer, wherein the first input value, the secondinput value and the third input value is associated with the first,second, and third genetic elements in the unknown sample; andclassifying the unknown sample as being associated with the biologicalstatus using the first boundary space or the second boundary space usingthe digital computer or the other digital computer.

EXAMPLES

199 plurality of test samples were labeled the “Long Beach datacollection.” In this data collection, the following target combinationswere tested: 1) orfx, mecA, and femA, 2) mecA, femA, and SCCmec, and 3)mecA, femA, SCCmec, and MR-Cons. The call algorithms developed for thisdata are based on a 2-dimensional elliptical mathematical model. In oneexample, the model generates an intermediate value. An MRSAclassification result is determined by thresholding on the intermediatevalue. The mathematical model is formulized as

${{\frac{\lbrack {{{- x} \cdot {\cos (\theta)}} + {y \cdot {\sin (\theta)}} - x_{0}} \rbrack^{2}}{a^{2}} + \frac{\lbrack {{x \cdot {\sin (\theta)}} + {y \cdot {\cos (\theta)}} - y_{0}} \rbrack^{2}}{b^{2}}} = S},$

where S in the intermediate value, and x and y are the two inputs tothis model. For the data collection of Orfx, femA, and mecA, x and y aremecA and femA, respectively. For the data collection of SCCmec, femA,and mecA, x and y are SCCmec and Y=f(mecA, femA), respectively. To bespecific, Y=f(mecA, femA)=mecA * sin(−0.3)+femA * cos(−0.3)., where -0.3is in radian. X₀, y₀, a, b and θ are predefined parameters, which areobtained by training this model with Genetic Algorithm with a givencriterion. Given x₀, y₀, a, b and θ, each pair of x and y will generatean S. A small S means that (x, y) are close to (x₀, y₀) and vice versa.For classification purposes, it is desirable to select a threshold of S(e.g., S₀). If a sample produces a less-than-S₀ intermediate value, thissample is deemed as MRSA positive. These call algorithms are based upona parameterized mathematical model, and are trained with a geneticalgorithm to reach the optimal performance, and generate classificationresults by thresholding on an intermediate value.

Sample Preparation

199 Nasal swabs were collected and stored in a stuart transfer medium.The swab heads were removed and each swab head was transferred into a 2ml sample suspension tube with 1200 μL of Tris based sample buffer with10 mM Tris pH 8.0 and 1 mM EDTA, pH 8.0˜100 mg of 1 mm Zirconia/Silicabeads. The bacteria on the swab heads were dislodged by vortexing thesample tubes at speed of 3000 rpm for at least 15 seconds.

The swab heads were then sterilely removed from the sample tubes andtransferred into 15 ml bacteria culture tubes with 1 ml of Trypic Soybroth TSB and 6.5% NaCl. The inoculated bacteria tubes were transferredinto a 37° C. incubator and incubated overnight with shaking at speed of200 rpm.

The presence or absence of Staphylococcus aureus and/or MRSA was thenconfirmed. 10 μL of each of the overnight culture broths was streaked onBBL™ CHROMagar MRSA and a BBL™ CHROMagar Staphylococcus aureus plate.500 μL of the 1200 μL sample solution from each tube was then subjectedto DNA isolation procedure as described by Agencourt VirNA kit protocol.This procedure, in brief, began with 200 CFU S. felis bacteria asprocess control. 10 units of Achrompeptidase were added to each tube,mixed well, and incubated in a 70° C. waterbath for 4 minutes. A freshprepared lysis solution 289 μL containing 188 μL of lysis buffer, 1.0 μLof PolyA (600 μg/ml), and 100 μL of protease K (6.4 mg/ml) was thenadded to each sample and mixed well. Each sample was then incubated at70° C. for 1 minute and then allowed to cool for 2 minutes. Then, 10 μLof magnetic beads and 575 μL of 100% isopropanol were added and mixedwell by vortexing. The reaction contents were allowed to incubate atroom temperature for 5 minutes, and then the magnetic beads werecollected by placing the sample tube on a magnet stand for 6 minutes toseparate the magnetic beads from the sample solution until the solutionbecome clear.

Next, the supernatant was aspirated off the samples while being carefulnot to remove any beads during aspiration. 500 μL of washing buffer(3.3M Guanidine Thiocyanate, 1.7% Triton X-100, 167.5 mM Sodium Citrate)were added to the samples and vortexed for 10 seconds to mix. The tubeswere then incubated on the magnet for 4 minutes (or until clear). Thesupernatant was then aspirated off the samples again. 900 μL of 75%freshly prepared ethanol was then added and vortexed for 10 seconds. Thetubes were then incubated on the magnet for 4 minutes until clear. Thesupernatant was then aspirated off the samples again and the ethanolwashing was repeated one more time. The beads were then dried on themagnet for 15-25 minutes. When the ring of the beads started to crack,the sample was eluted. The tubes were taken off the magnet and 25 μL ofnuclease free water was added. The samples were then vortexed to mix.The tubes were then incubated for 5 minutes at 70° C. The tubes wereplaced back on the magnet and incubated for 1 minute. The supernatantwas then transferred to a clean tube for PCR amplification.

PCR Primers and Probes, PCR Cycling Conditions

The reagents listed in the Master mix table were prepared on ice.According to the total reaction number, enough Master mix can beprepared by simply adding the indicated volumes of reagents together ina DNA/RNA/RNase-free tube. The tubes can be vortexed to mix and thenleft on ice for later use. 20 μL of each eluent was added to a Mx3000P96-well PCR plate (non-skirted) (Stratagene, Cat#401333) (one eluent,one well). 30 μL Master mix was added to each well filled with theeluent, and then mixed by gently pipetting up and down 8 times or more(a multi-channel might be useful.). The plate was covered tightly withMicroAmp™ optical adhesive film (Applied Biosystems), and thencentrifuged at 1100×g for 3 minutes before it is put into the PCRmachine.

The PCR cycling conditions on the Stratagene MX3005P instrument were setas follows: 4′ @ 37° C. (1×); 1 min @ 95° C. (1×); 15 sec @ 95° C.→10sec @62° C.→30 sec @ 58° C. (40×). The targets monitored are representedin Table 1 below:

Target Function Oligo ID Sequence orfX-ISS Forward primerOrfX-ISS/attBScc TGAGGGTTGTGTTAATTGAGCAAGTG for-1 Forward primerOrfX-ISS/attBScc TGCGGGTTGTGTTAATTGAACAAGTG for-2 Reverse primermecII512-sccmec- TCACTTTTTATTCTTCAAAGATTTGAGC 3 Reverse primerprimer 11-1-sccmec- AAATTGCTACTAAAGAGGATATGGAAAACCATC 7 Reverse primerprimer12-sccmec-8  CTCTGCTTTATATTATAAAATTACGGCTG Reverse primernewtypeiii-1- CGTATGATATTGCAAGGTATAATCCAATATTTC sccmec-14 Reverse primertypelVc-sccemc-2 CTTGAAATGAAAGACTGCGGAGGCTAAC Reverse primer NEWPRIMERSTGAGCTTTTTCCACTCCCATTTCTTCCAAA Reverse primer SCCmec-4nVGCAATTCACATAAACCTCATATGTTCTGATAC Reverse primer SCCmec-3nCATTCATTCATCCACCCTAAACTTAATCTTTC Reverse primer SCCmec-5nTATGGAAATCCATCTCTACTTTATTGTTTTCTTC Reverse primer SCCmec-6nAATATTTCATATATGTAATTCCTCCACATCTC Reverse primer SE-7-11CTATTTCTGTAATACTTAAAACCTTTTCTTCC Reverse primer SE-17CCGTATGATTCATATTAAAATGAATCATACGGAGG Reverse primer SE-13CTTCTTATGAAATGTCTTTTTTCACTTATCC TaqMan probe orfx probe-2ATGCTTCTCCTCGCATAATCTTAAAYGCTC TaqMan probe ORFX PROBE-1ACGCTTCTCCACGCATAATCTTAAATGCTC TaqMan probe ORFX PROBEACGCCTCTCCTCGCATAATCTTAAATGCTC femA Forward primer femA-3 forwardGACCGTTATAATTTCTATGGTGTTAGTGG primer Reverse primer femA-3 reverseGTCACCAACATATTCAATAATTTCAGC primer TaqMan probe femA-sa-probeACAGAAGATGCTGAAGATGCTGGTGT mecA Forward primer mecA-2 forwardGCAGAAAGACCAAAGCATACATATTGA primer Reverse primer MecA-2 reverseGCCTATCTCATATGCTGTTCCTGT primer TaqMan probe mecAprobeAGACCGAAACAATGTGGAATTGGCCA S. felis IC forward SfforwardnewTGCCAATGTAGATAGTCTTCCAGA (IC) primer IC reverse sfreversenewAAGTGCCCAGAAGAATGAGTGG primer IC probe fSfelisACCGCCACCATTATTACGTACAGCTG SCCmec Forward primer OrtX-ISS/attBSccTGAGGGTTGTGTTAATTGAGCAAGTG for-1 Forward primer OrtX-ISS/attBSccTGCGGGTTGTGTTAATTGAACAAGTG for-2 Reverse primer mecII512-1-sccmec-TCACTTTTTATTCTTCAAAGATTTGAGC 3 Reverse primer primer11-1-sccmec-AAATTGCTACTAAAGAGGATATGGAAAACCATC 7 Reverse primer primer12-sccmec-8CTCTGCTTTATATTATAAAATTACGGCTG Reverse primer newtypeiii-1-CGTATGATATTGCAAGGTATAATCCAATATTTC sccmec-14 Reverse primertypelVc-sccemc-2 CTTGAAATGAAAGACTGCGGAGGCTAAC Reverse primer NEWPRIMERSTGAGCTTTTTCCACTCCCATTTCTTCCAAA Reverse primer SCCmec-4nVGCAATTCACATAAACCTCATATGTTCTGATAC Reverse primer SCCmec-3nCATTCATTCATCCACCCTAAACTTAATCTTTC Reverse primer SCCmec-5nTATGGAAATCCATCTCTACTTTATTGTTTTCTTC Reverse primer SCCmec-6nAATATTTCATATATGTAATTCCTCCACATCTC Reverse primer SE-7-11CTATTTCTGTAATACTTAAAACCTTTTCTTCC Reverse primer SE-17CCGTATGATTCATATTAAAATGAATCATACGGAGG Reverse primer SE-13CTTCTTATGAAATGTCTTTTTTCACTTATCC TaqMan probe orfx probe-2ATGCTTCTCCTCGCATAATCTTAAAYGCTC TaqMan probe ORFX PROBE-1ACGCTTCTCCACGCATAATCTTAAATGCTC TaqMan probe ORFX PROBEACGCCTCTCCTCGCATAATCTTAAATGCTC

In the table above, the probes that have sequences that correspond toSCCmec are complementary to the right extremity of SCCmec.

The table below shows false positive and false negative data generatedusing 199 samples in the Long Beach data collection and using analyticalmodels according to embodiments of the invention (as described above)and analytical models using other call algorithms.

Enrichment Culture Data 1 (59 pos, 140 neg) FP FN  1: Strategy A3-Target clustering (mecA, femA, orfX) 10 2 DxN calling algorithm  2:Strategy C 4-Target clustering (mecA, femA, SCCmec 3 2 DxN callingalgorithm   (MRSA), SCCmec (MR-ConS))  3: Strategy C 3-Target clustering(mecA, femA, SCCmec) 3 4 DxN calling algorithm  4: Xpert-1 Target(SCCmec) 11 2  5: Strategy C 1-Target (SCCmec) 11 4 Published callalgorithm  6: Strategy C 2-Target (SCCmec, mecA) 10 5 Published callalgorithm  7: Strategy C (SCCmec, mecA) Ct comparison (±4ct) 5 7Published call algorithm  8: Strategy C 2-Target (SCCmec, femA) 3 5Published call algorithm  9: Strategy C (SCCmec, femA) Ct Comparison 3 5Published call algorithm 10: Strategy C 3-Target (SCCmec, mecA and femA)2 5 Published call algorithm 11: Strategy A 2 target (femA and mecA) 381 Published call algorithm 12: Strategy A 2 target (orfX - ve) Ctcomparison (±4ct) 24 5 Published call algorithm 13: Strategy A 3 targets(mecA, femA, orfX) 18 25 Published call algorithm 14: Strategy A 3targets (mecA, femA, orfX) Ct comparison 17 5 Published call algorithm  (±4ct)

The first three analytical models (1 to 3) are those produced accordingto embodiments of the invention, and show good results. The firstanalytical model entitled Strategy A using three target clustering andthe process described above (e.g., as in FIG. 1) yielded 10 falsepositives and 2 false negatives. The second analytical model usingStrategy C and 4 targets yielded 3 false positives and 2 falsenegatives. The third analytical model using Strategy C yielded 3 falsepositives and 4 false negatives. The methods associated with Strategy Aand Strategy C are described above (e.g., as in FIG. 5). In the case ofthe 4 target Strategy C example, MRConS was used as additional target todifferentiate MRSA samples from non-MRSA samples.

The remaining data shows data that was processed according to publishedcall algorithms. Some of the details of such algorithms are providedbelow.

For 4: Xpert is a known and commercially available test.

For 5: Strategy C 1-target; SCCmec is the target. If an SCCmec Ct valueis less than 32, then MRSA is present. If the SCCmec Ct value is largerthan 32, then there is no MRSA.

For 6: Strategy C 2-Target (SCCmec, mecA): If both SCCmec and mecA areless than 32, then MRSA is present. If SCCmec is larger than 32 or mecAlarger than 32, then there is no MRSA.

For 7: Strategy C (SCCmec, mecA) Ct comparison (±4 ct): If SCCmec islarger than 32 or mecA is larger than 32, then there is no MRSA. If bothSCCmec and mecA Ct values are less than 32, and the delta Ct betweenSCCmec and mecA is less than 4, then MRSA is present. If the delta Ct islarger than 4, then MRSA is not present.

For 8: Strategy C 2-Target (SCCmec, femA): If both SCCmec and femA areless than 32, then MRSA is present. If SCCmec is larger than 32 or femAis larger than 32, then there is no MRSA.

For 9: Strategy C (SCCmec, femA) Ct Comparison: If SCCmec is larger than32 or femA is larger than 32, then there is no MRSA. If both SCCmec andfemA Ct values are less than 32, and the delta Ct between SCCmec andfemA is less than 4, then MRSA is present. If the delta Ct is largerthan 4, then MRSA is not present.

For 10: Strategy C 3-Target (SCCmec, mecA and femA): If SCCmec, mecA,femA are larger than 32, then MRSA is not present. If mecA is largerthan 32, then there is no MRSA. If SCCmec is larger than 32, then noMRSA is present. If mecA and femA are both less than 32, and SCCmec islarger than 32, and the delta Ct value of mecA and femA is less than 4,MRSA is present.

For 11: Strategy A 2 target (femA and mecA): If both femA and mecA areless than 32, then MRSA is present. Otherwise, there is no MRSA.

For 12: Strategy A 2 target (orfX-ye) Ct comparison (±4 ct): If bothfemA and mecA are less than 32 and the delta Ct between femA and mecA isless than 4, then MRSA is present. Otherwise, there is no MRSA.

For 13: Strategy A 3 targets (mecA, femA, orfX):): If both femA and mecAare less than 32 and orfX is negative, then MRSA is present. Otherwise,there is no MRSA.

For 14: Strategy A 3 targets (mecA, femA, orfX) Ct comparison (±4 ct):):If both femA and mecA are less than 32 and orfX is negative, and thedelta Ct value between mecA and femA is less than 4, then MRSA ispresent. Otherwise, there is no MRSA.

The software components, steps, or functions described in thisapplication, may be implemented as software code to be executed by oneor more processors using any suitable computer language such as, forexample, Java, C++ or Perl using, for example, conventional orobject-oriented techniques. The software code may be stored as a seriesof instructions, or commands on a computer readable medium, such as arandom access memory (RAM), a read only memory (ROM), a magnetic mediumsuch as a hard-drive or a floppy disk, or an optical medium such as aCD-ROM. Any such computer readable medium may also reside on or within asingle computational apparatus, and may be present on or withindifferent computational apparatuses within a system or network.

Some embodiments of the present invention can be implemented in the formof control logic in software or hardware or a combination of both. Thecontrol logic may be stored in an information storage medium as aplurality of instructions adapted to direct an information processingdevice to perform a set of steps disclosed in an embodiment of thepresent invention. Based on the disclosure and teachings providedherein, a person of ordinary skill in the art will appreciate other waysand/or methods to implement the present invention.

Any recitation of “a”, “an” or “the” is intended to mean “one or more”unless specifically indicated to the contrary.

The above description is illustrative and is not restrictive. Manyvariations of the invention will become apparent to those skilled in theart upon review of the disclosure. The scope of the invention should,therefore, be determined not with reference to the above description,but instead should be determined with reference to the pending claimsalong with their full scope or equivalents.

All patents, patent applications, publications, and descriptionsmentioned above are herein incorporated by reference in their entiretyfor all purposes. None is admitted to be prior art.

1. A method comprising: entering into a digital computer, at least aplurality of first input values associated with a first genetic element,a plurality of second input values associated with a second geneticelement, and a plurality of third input values associated with a thirdgenetic element associated with a plurality of known samples, whereineach known sample includes a first input value in the plurality of firstinput values, a second input value in the plurality of second inputvalues, and a third input value in the plurality of third input values;determining a threshold value associated with the third genetic element;separating the known samples using the threshold value into a first setof known samples and a second set of known samples; clustering the firstset of known samples in a feature space defined by the first geneticelement and the second genetic element; defining a first boundary spaceusing the first set of known samples, wherein the first boundary spacedifferentiates a biological status from other biological statuses; anddefining a second boundary space using the second set of known samples,wherein the second boundary space differentiates the biological statusfrom other biological statuses.
 2. The method of claim 1 wherein thefirst genetic element comprises mecA, the second genetic elementcomprises femA, and the third genetic element comprises OrfX, andwherein the biological status comprises an MRSA status.
 3. The method ofclaim 1 wherein the first boundary space and the second boundary spaceare defined by first and second ellipses, respectively.
 4. The method ofclaim 1 wherein the first, second and third values are Ct values.
 5. Themethod of claim 1 further comprising, prior to entering: subjecting theknown samples to conditions that will expose nucleic acids in the knownsamples; and amplifying and detecting the presence and amounts of atleast the first genetic element, the second genetic element and thethird genetic element.
 6. A computer readable medium comprising codeexecutable by a processor, for implementing a method comprising.entering into a digital computer, at least a plurality of first inputvalues associated with a first genetic element, a plurality of secondinput values associated with a second genetic element, and a pluralityof third input values associated with a third genetic element associatedwith a plurality of known samples, wherein each known sample includes afirst input value in the plurality of first input values, a second inputvalue in the plurality of second input values, and a third input valuein the plurality of third input values; determining a threshold valueassociated with the third genetic element; separating the known samplesusing the threshold value into a first set of known samples and a secondset of known samples; clustering the first set of known samples in afeature space defined by the first genetic element and the secondgenetic element; defining a first boundary space using the first set ofknown samples, wherein the first boundary space differentiates abiological status from other biological statuses; and defining a secondboundary space using the second set of known samples, wherein the secondboundary space differentiates the biological status from otherbiological statuses.
 7. A digital computer comprising the computerreadable medium of claim 7, and a processor coupled to the computerreadable medium.
 8. A system comprising a measuring module coupled tothe digital computer of claim
 7. 9. A method for using an analyticalmodel created according to the method of claim 1 wherein the method forusing the analytical model comprises: entering a first input value,second input value, and third input value associated with an unknownsample into the digital computer or other digital computer, wherein thefirst input value, the second input value and the third input value isassociated with the first, second, and third genetic elements in theunknown sample; and classifying the unknown sample as being associatedwith the biological status using the first boundary space or the secondboundary space using the digital computer or other digital computer. 10.The method of claim 9 wherein the biological status comprises a MRSAstatus.
 11. A method of creating an analytical model, whichdifferentiates a biological status from other biological statuses, themethod comprising: entering, into a digital computer, at least aplurality of first input values associated with a first genetic element,a plurality of second input values associated with a second geneticelement, and a plurality of third input values associated with a thirdgenetic element; creating one or more intermediate values using at leastthe plurality of first input values and at least the second input valuesassociated with a second genetic element using the digital computer; andcreating a boundary space for the biological status using the one ormore intermediate values and the plurality of third input values usingthe digital computer, wherein the boundary space differentiates thebiological status from other biological statuses.
 12. The method ofclaim 11 wherein the first genetic element comprises mecA, the secondgenetic element comprises femA, and the third genetic element comprisesSCCmec, and wherein the biological status comprises an MRSA status. 13.The method of claim 11 wherein the boundary space is defined by anellipse.
 14. The method of claim 13 wherein a cost function is used tooptimize the ellipse.
 15. The method of claim 11 wherein the first,second and third values are Ct values.
 16. The method of claim 11further comprising, prior to entering: subjecting the samples toconditions that will expose nucleic acids in the samples; and amplifyingand detecting the presence and amounts of at least the first geneticelement, the second genetic element and the third genetic element.
 17. Acomputer readable medium comprising code executable by a processor, forimplementing a method comprising: entering, into a digital computer, atleast a plurality of first input values associated with a first geneticelement, a plurality of second input values associated with a secondgenetic element, and a plurality of third input values associated with athird genetic element; creating one or more intermediate values using atleast the plurality of first input values and at least the second inputvalues associated with a second genetic element using the digitalcomputer; and creating a boundary space for the biological status usingthe one or more intermediate values and the plurality of third inputvalues using the digital computer, wherein the boundary spacedifferentiates the biological status from other biological statuses. 18.A digital computer comprising the computer readable medium of claim 11,and a processor coupled to the computer readable medium.
 19. A systemcomprising a measuring module coupled to the digital computer of claim18.
 20. A method for using an analytical model created according to themethod of claim 11 wherein the method for using the analytical modelcomprises: entering a first input value, second input value, and thirdinput value associated with an unknown sample into the digital computeror other digital computer, wherein the first input value, the secondinput value and the third input value is associated with the first,second, and third genetic elements in the unknown sample; andclassifying the unknown sample as being associated with the biologicalstatus using the boundary space.
 21. A method for determining thepresence of methicillin-resistant Staphylococcus aureus (MRSA) in asample, the method comprising: subjecting the sample to conditions thatwill expose the nucleic acids of any bacteria present in the sample;amplifying and detecting the presence and amounts of at least mecA,orfX, and a Staphylococcus aureus-specific target gene sequence in thesample; and determining the presence of MRSA in the sample by executinga call algorithm on a digital computer, wherein the call algorithm usesas inputs at least the detected and measured amounts of mecA, orfX, andthe Staphylococcus aureus-specific target gene sequence to determinewhether MRSA is present in the sample, wherein the call algorithm uses afirst boundary space to determine the presence of MRSA when the detectedamount of orfX is below a threshold value, wherein the call algorithmuses a second boundary space to determine the presence of MRSA when thedetected amount of orfX is above the threshold value. 22.-32. (canceled)33. A system for determining the presence of methicillin-resistantStaphylococcus aureus (MRSA) in a sample, the system comprising: ameasuring module capable of amplifying and detecting the presence andamounts of at least mecA, orfX, and a Staphylococcus aureus-specifictarget gene sequence in the sample, wherein the sample has beensubjected to conditions that will expose the nucleic acids of anybacteria present in the sample; a memory to store the detected amountsfrom the measuring module; a computer readable medium containingcomputer readable code having instructions for executing a callalgorithm, wherein the call algorithm uses as inputs at least thedetected and measured amounts of mecA, orfX, and the Staphylococcusaureus-specific target gene sequence to determine whether MRSA ispresent in the sample, wherein the call algorithm uses a first boundaryspace to determine the presence of MRSA when the detected amount of orfXis below a threshold value, wherein the call algorithm uses a secondboundary space to determine the presence of MRSA when the detectedamount of orfX is above the threshold value; and a processor to executethe computer readable code on the computer readable medium in order todetermine the presence MRSA in the sample. 34.-44. (canceled)
 45. Acomputer-readable medium comprising: code for a call algorithm, whereinthe call algorithm uses as inputs at least the detected and measuredamounts of mecA, orfX, and a Staphylococcus aureus-specific target genesequence to determine whether MRSA is present in the sample, wherein thecall algorithm uses a first boundary space to determine the presence ofMRSA when the detected amount of orfX is below a threshold value,wherein the call algorithm uses a second boundary space to determine thepresence of MRSA when the detected amount of orfX is above the thresholdvalue.
 46. A method for determining the presence ofmethicillin-resistant Staphylococcus aureus (MRSA) in a sample, themethod comprising: subjecting the sample to conditions that will exposethe nucleic acids of any bacteria present in the sample; amplifying anddetecting the presence and amounts of at least mecA, SCCmec, and aStaphylococcus aureus-specific target gene sequence (SA) in the sample;and determining the presence of MRSA in the sample by executing a callalgorithm on a digital computer, wherein the call algorithm uses asinputs at least the detected and measured amounts of mecA, SCCmec, andthe Staphylococcus aureus-specific target gene sequence to determinewhether MRSA is present in the sample, wherein the call algorithmcreates an intermediate value from at least the Staphylococcusaureus-specific target gene sequence and mecA, wherein the callalgorithm further uses a boundary space to determine the presence ofMRSA, wherein the boundary space is defined using the intermediate valueand SCCmec. 47.-54. (canceled)
 55. A system for determining the presenceof methicillin-resistant Staphylococcus aureus (MRSA) in a sample, thesystem comprising: a measuring module capable of amplifying anddetecting the presence and amounts of at least mecA, SCCmec, and aStaphylococcus aureus-specific target gene sequence (SA) in the sample,wherein the sample has been subjected to conditions that will expose thenucleic acids of any bacteria present in the sample; a memory to storethe detected amounts from the measuring module; a computer readablemedium containing computer readable code having instructions forexecuting a call algorithm, wherein the call algorithm uses as inputs atleast the detected and measured amounts of mecA, SCCmec, and theStaphylococcus aureus-specific target gene sequence to determine whetherMRSA is present in the sample, wherein the call algorithm creates anintermediate value from at least mecA and the Staphylococcusaureus-specific target gene sequence, wherein the call algorithm furtheruses a boundary space to determine the presence of MRSA, wherein theboundary space is defined using the intermediate value and SCCmec; and aprocessor to execute the computer readable code on the computer readablemedium in order to determine the presence MRSA in the sample. 56.-63.(canceled)
 64. A computer-readable medium comprising: code for a callalgorithm, wherein the call algorithm uses as inputs at least thedetected and measured amounts of mecA, SCCmec, and a Staphylococcusaureus-specific target gene sequence to determine whether MRSA ispresent in the sample, wherein the call algorithm creates anintermediate value from at least mecA and the Staphylococcusaureus-specific target gene sequence, wherein the call algorithm furtheruses a boundary space to determine the presence of MRSA, wherein theboundary space is defined using the intermediate value and SCCmec.
 65. Amethod for creating a model that can be used to determine the presenceof methicillin-resistant Staphylococcus aureus (MRSA) in an unknownsample, the method comprising: subjecting a set of known samples toconditions that will expose the nucleic acids of any bacteria present inthe known samples, wherein the presence of MRSA is known for each samplein the set of known samples; amplifying and detecting the presence andamounts of at least mecA, orfX, and a Staphylococcus aureus(SA)-specific target gene sequence in the known samples; executing acall algorithm on a digital computer for each sample in the knownsamples, wherein the call algorithm uses as inputs the detected andmeasured amounts of mecA, orfX, and the Staphylococcus aureus-specifictarget gene sequence; and creating a model that can be used to determinewhether MRSA is present in the unknown sample, wherein the model iscreated from the output of the call algorithm executed against the knownsamples, wherein the model uses a first boundary space to determine thepresence of MRSA when the detected amount of orfX is below a thresholdvalue, wherein the model uses a second boundary space to determine thepresence of MRSA when the detected amount of orfX is above the thresholdvalue.
 66. A method for creating a model that can be used to determinethe presence of methicillin-resistant Staphylococcus aureus (MRSA) in anunknown sample, the method comprising: subjecting a set of known samplesto conditions that will expose the nucleic acids of any bacteria presentin the known samples, wherein the presence of MRSA is known for eachsample in the set of known samples; amplifying and detecting thepresence and amounts of at least mecA, SCCmec, and a Staphylococcusaureus (SA)-specific target gene sequence in the known samples;executing a call algorithm on a digital computer for each sample in theknown samples, wherein the call algorithm uses as inputs the detectedand measured amounts of mecA, SCCmec, and the Staphylococcusaureus-specific target gene sequence; and creating a model that can beused to determine whether MRSA is present in the unknown sample, whereinthe model is created from the output of the call algorithm executedagainst the known samples, wherein the model creates an intermediatevalue from at least the Staphylococcus aureus-specific target genesequence and mecA, wherein the call algorithm further uses a boundaryspace to determine the presence of MRSA, wherein the boundary space isdefined using the intermediate value and SCCmec.